Development of a decision-support system to select nature-based solutions for domestic wastewater treatment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Nature-based solutions are increasingly used in domestic wastewater treatment, because of their potential to remove contaminants and pathogens from water (e.g., stormwater, river water, wastewater) as well as their provided co-benefits, such as mitigation of the heat island effect or enhanced biodiversity. The transition from traditional grey technologies towards nature-based solutions in domestic wastewater treatment might yield multiple benefits for local communities while enhancing biodiversity. Although some nature-based solutions such as treatment wetlands have been used for decades in domestic wastewater treatment, this is not the case for others such as green walls or roofs, which lack implementation guidelines and design criteria. Aiming to support implementation of nature-based solutions in domestic wastewater treatment, we have developed an online decision-support system for the pre-selection of the best nature-based solution to use in each socio-environmental context and adapted to the needs, as well as an estimate of the required area. Our decision-support system's recommendations are based on an expert knowledge-driven approach, building on two complementary expert knowledge elicitation workshops. We hope the developed online decision-support system will support the transition towards integrating nature-based solutions into urban water and wastewater treatment systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it